Apparatus and method for relativistic event perception prediction and content creation

Abstract

An apparatus, method, and computer program product are provided for the improved and automatic prediction of a relativistic, observer-specific perception and response to a potential event and, based at least in part on the predicted perception and response, generating and presenting observer-specific digital content items. Some example implementations employ predictive, machine-learning modeling to facilitate user-specific event perception and response prediction and the selection of particularized messages and other digital content items for presentation to the user.

What is claimed is: 
     
       1. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to:
 retrieve a user-specific digital content item set for a user comprising a first user-specific message, wherein retrieving the user-specific digital content item set comprises applying a user context data object, an event probability data object, and an event perception data object to a first model, wherein retrieving the user-specific digital content item set comprises:
 generating, by the first model, a learned user profile; 
 applying the learned user profile to a plurality of potential messages; 
 selecting the first user-specific message from the plurality of potential messages; and 
 assigning the first user-specific message to the user-specific digital content item set; and 
 
 generate a control signal causing a renderable object comprising the user-specific digital content item set to be displayed on a user interface of a client device associated with the user. 
 
     
     
       2. The apparatus of  claim 1 , wherein the user-specific digital content item set further comprises a first user-selectable option set, and wherein retrieving the user-specific digital content item set further comprises:
 applying the learned user profile to a plurality of user-selectable options; 
 selecting the first user-selectable option set from among a plurality of user-selectable options; and 
 assigning the first user-selectable option set to the user-specific digital content item set. 
 
     
     
       3. The apparatus of  claim 1 , wherein the event perception data object comprises a user-specific event perception profile based at least in apart on a user-generated content set, the user-generated content set comprising data associated with one or more interactions between the user and one or more social media platforms. 
     
     
       4. The apparatus of  claim 1 , wherein the event probability data object comprises real-time event data based on one or more of contemporaneous weather reports, contemporaneous social media posts, and contemporaneous news records. 
     
     
       5. The apparatus of  claim 4 , wherein the event probability data object further comprises historical event data based on one or more of historical event records, historical footprint data, and historical event frequency data. 
     
     
       6. The apparatus of  claim 5 , wherein at least one of the real-time event data or the historical event data further comprises data associated with emergency service telephonic records. 
     
     
       7. The apparatus of  claim 1 , wherein applying the learned user profile to the plurality of potential messages comprises:
 determining a score for each potential message of the plurality of potential messages based on an application of profile weights associated with the learned user profile; 
 wherein the first user-specific message is selected from the plurality of potential messages based the first user-specific message having a score greater than each other potential message of the plurality of potential messages. 
 
     
     
       8. The apparatus of  claim 1 , wherein the user-specific digital content item set is retrieved in response to receiving a message request data object from the client device associated with the user. 
     
     
       9. A non-transitory computer-readable medium storing computer-executable instructions for:
 retrieving a user-specific digital content item set for a user comprising a first user-specific message, wherein retrieving the user-specific digital content item set comprises applying a user context data object, an event probability data object, and an event perception data object to a first model, wherein retrieving the user-specific digital content item set comprises:
 generating, by the first model, a learned user profile; 
 applying the learned user profile to a plurality of potential messages; 
 selecting the first user-specific message from the plurality of potential messages; and 
 assigning the first user-specific message to the user-specific digital content item set; and 
 
 generating a control signal causing a renderable object comprising the user-specific digital content item set to be displayed on a user interface of a client device associated with the user. 
 
     
     
       10. The non-transitory computer-readable medium of  claim 9 , wherein the user-specific digital content item set further comprises a first user-selectable option set, and wherein retrieving the user-specific digital content item set further comprises:
 applying the learned user profile to a plurality of user-selectable options; 
 selecting the first user-selectable option set from among a plurality of user-selectable options; and 
 assigning the first user-selectable option set to the user-specific digital content item set. 
 
     
     
       11. The non-transitory computer-readable medium of  claim 9 , wherein the event perception data object comprises a user-specific event perception profile based at least in apart on a user-generated content set, the user-generated content set comprising data associated with one or more interactions between the user and one or more social media platforms. 
     
     
       12. The non-transitory computer-readable medium of  claim 9 , wherein applying the learned user profile to the plurality of potential messages comprises:
 determining a score for each potential message of the plurality of potential messages based on an application of profile weights associated with the learned user profile; 
 wherein the first user-specific message is selected from the plurality of potential messages based the first user-specific message having a score greater than each other potential message of the plurality of potential messages. 
 
     
     
       13. A method comprising:
 retrieving a user-specific digital content item set for a user comprising a first user-specific message, wherein retrieving the user-specific digital content item set comprises applying a user context data object, an event probability data object, and an event perception data object to a first model, wherein retrieving the user-specific digital content item set comprises:
 generating, by the first model, a learned user profile; 
 applying the learned user profile to a plurality of potential messages; 
 selecting the first user-specific message from the plurality of potential messages; and 
 assigning the first user-specific message to the user-specific digital content item set; and 
 
 generating a control signal causing a renderable object comprising the user-specific digital content item set to be displayed on a user interface of a client device associated with the user. 
 
     
     
       14. The method of  claim 13 , wherein the user-specific digital content item set further comprises a first user-selectable option set, and wherein retrieving the user-specific digital content item set further comprises:
 applying the learned user profile to a plurality of user-selectable options; 
 selecting the first user-selectable option set from among a plurality of user-selectable options; and 
 assigning the first user-selectable option set to the user-specific digital content item set. 
 
     
     
       15. The method of  claim 13 , wherein the event perception data object comprises a user-specific event perception profile based at least in apart on a user-generated content set, the user-generated content set comprising data associated with one or more interactions between the user and one or more social media platforms. 
     
     
       16. The method of  claim 13 , wherein the event probability data object comprises real-time event data based on one or more of contemporaneous weather reports, contemporaneous social media posts, and contemporaneous news records. 
     
     
       17. The method of  claim 16 , wherein the event probability data object further comprises historical event data based on one or more of historical event records, historical footprint data, and historical event frequency data. 
     
     
       18. The method of  claim 17 , wherein at least one of the real-time event data or the historical event data further comprises data associated with emergency service telephonic records. 
     
     
       19. The method of  claim 13 , wherein applying the learned user profile to the plurality of potential messages comprises:
 determining a score for each potential message of the plurality of potential messages based on an application of profile weights associated with the learned user profile; 
 wherein the first user-specific message is selected from the plurality of potential messages based the first user-specific message having a score greater than each other potential message of the plurality of potential messages. 
 
     
     
       20. The method of  claim 13 , wherein the user-specific digital content item set is retrieved in response to receiving a message request data object from the client device associated with the user.